Análise dos CIDs

knitr::opts_chunk$set(echo = T)
options(scipen=100)
options(digits=2)

library(readr)
library(tidyverse)

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## v ggplot2 3.0.0     v forcats 0.3.0

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library(plotly)

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cids <- read_csv("dados_cs/cids_mais_comuns.csv")

## Parsed with column specification:
## cols(
##   DISTRITO = col_character(),
##   UNIDADE = col_character(),
##   ANO = col_integer(),
##   MES = col_integer(),
##   ESPECIALIDADE = col_character(),
##   `COD_CID-10` = col_character(),
##   `DESCRICAO_CID-10` = col_character(),
##   QTDE = col_integer(),
##   ORDEM_CID = col_integer()
## )

cids$MES <- as.character(cids$MES)
cids$MES <- if_else(nchar(cids$MES) ==1, paste0(0,cids$MES), cids$MES)
cids$ANO_MES <- paste0(cids$ANO, "-", cids$MES)
cids_agregados <- aggregate(cids$QTDE, by = list(cids$ANO_MES, cids$`COD_CID-10`), FUN = sum)
names(cids_agregados) <- c("ANO_MES", "CID", "VALOR")

banco <- subset(cids_agregados,cids_agregados$CID == "A09")
a <- ggplot(banco)+
        geom_line(aes(ANO_MES, VALOR, group = 1), col = "blue")+
        ylab("Valor")+
        xlab("Ano-Mês")+
        ggtitle("Série Temporal - CIDS")+
        theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplotly(a)

b <- ggplot(cids_agregados)+
        geom_line(aes(ANO_MES, VALOR, group = CID, color = cids_agregados$CID))+
        ylab("Valor")+
        xlab("Ano-Mês")+
        ggtitle("Série Temporal - CIDS")+
        theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplotly(b)

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